Exploiting Nicotinic Receptor Mechanisms for the Treatment of Schizophrenia and Depression
Bibliographic record
Abstract
Smoking rates are higher in persons with schizophrenia (SZ; 58%–88%) and major depressive disorder (MDD; 40%–60%) compared to the U.S. general population (∼23%). Nicotinic acetylcholine receptors (nAChRs) are the brain receptors for nicotine. SZ and MDD are nicotine-responsive neuropsychiatric disorders. Thus, it is hypothesized that the higher rates of smoking in the SZ and MDD populations can be attributed to the pathophysiology of these disorders. Knowledge of nAChR functioning can be exploited in therapeutics for treating both the addiction and clinical aspects of these disorders. This article reviews the neurobiology of nAChR and biological dysregulation inherent in SZ and MDD. In addition, manipulation of nAChRs with appropriate agonists and antagonists is discussed. Specifically, nAChRs can be stimulated to improve cognitive deficits associated with SZ and blocked for the treatment of selective serotonin reuptake inhibitor–refractory major depression; a combination of agonism and antagonism may assist with smoking cessation in SZ populations. This knowledge has significant implications for further development of pharmacotherapies for treatment of SZ and MDD symptoms and for smoking cessation in these disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".